Wavefront Correction Technology Based on Fuzzy Control

Zhangwen, 刘章文 Liu; Zhiqiang, 周志强 Zhou; Zhengdong, 李正东 Li

doi:10.3788/lop54.030101

Cited by 2 publications

(1 citation statement)

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“…It was believed that the technical department was subject to the response model of the deformable mirror, and the wave front correction effect required by the adaptive engineering system could be realized by evaluating the fuzzy proportional integral differential control. Experiments showed that the technique could achieve good control effects and accuracy [ 10 ]. Heng et al mainly discussed the use of fuzzy control technology to improve the output accuracy of the missile's inertial component unit in the dynamic environment.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Measurement and Analysis of Sewage Treatment Parameters based on Fuzzy Neural Algorithm with ARM9 Core CPU

2022

Computational Intelligence and Neuroscience

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After entering the new century, the state continues to increase the construction of urban sewage treatment projects in response to the deteriorating water pollution situation. How to collect and analyze the sewage parameter variables in the sewage treatment process to ensure the intelligent measurement and accurate operation of the parameters in the treatment application is an urgent problem to be solved. This paper is mainly based on the computer-aided control system built by the ARM9 core embedded chip, and the feasibility and effectiveness of fuzzy neural network algorithms are discussed to improve the intelligent processing of sewage treatment parameters. After analyzing the principle and implementation flow of fuzzy control and neural network, starting from the characteristics of data collected by ARM9 core chip, the hybrid algorithm model is optimized and improved to further improve the convergence speed and accuracy of the algorithm. The simulation experiment proves that the optimized fuzzy neural control algorithm can effectively identify the dissolved oxygen, nitrate nitrogen, and other parameter data in the sewage treatment, and the recognition accuracy is very close to the true precision. Based on biosensors, the ARM9 core chip control system established by a recursive fuzzy neural network can greatly improve the tracking and control ability of parameters such as dissolved oxygen concentration and nitrate nitrogen concentration in microbial degradation. This has a good development prospect in wastewater treatment control applications. The experimental results show that the recursive fuzzy neural network algorithm proposed in this paper can dynamically track and control the nitrate concentration and dissolved oxygen concentration and ensure that the control is within the accuracy range. The accuracy of recognition is very close to the real accuracy.

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Section: Related Workmentioning

confidence: 99%

Intelligent Measurement and Analysis of Sewage Treatment Parameters based on Fuzzy Neural Algorithm with ARM9 Core CPU

2022

Computational Intelligence and Neuroscience

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A novel control method for roll gap of roller crusher based on Fuzzy-PID with decision factor self-correction

Yang

Fan

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Roller crusher is widely used in solid and block material pulverizing. Roll gap between the rollers is usually adjusted to satisfy the particle size of output material. In order to realize convenient adjustment, a roll gap control scheme based on electro-hydraulic technology is designed and optimized. And Fuzzy-PID is applied to realize the automatic process. Moreover, decision factor self-correction is introduced to balance the number of Fuzzy rules and system precision. Finally, an experiment platform and some simulations are conducted to validate the performance of the proposed approach.

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Wavefront Correction Technology Based on Fuzzy Control

Cited by 2 publications

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Intelligent Measurement and Analysis of Sewage Treatment Parameters based on Fuzzy Neural Algorithm with ARM9 Core CPU

Intelligent Measurement and Analysis of Sewage Treatment Parameters based on Fuzzy Neural Algorithm with ARM9 Core CPU

A novel control method for roll gap of roller crusher based on Fuzzy-PID with decision factor self-correction

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